Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/11367

TitleMining healthcare data : the case of an endoscopic thoracic sympathectomy dataset
Author(s)Santos, Maribel Yasmina
Gonçalves, Diana
Cruz, Jorge M.
KeywordsKnowledge discovery in databases
Data mining
Decision trees
Primary hyperhidrosis
Endoscopic thoracic sympathectomy
Issue date2010
PublisherWorld Scientific and Engineering Academy and Society (WSEAS)
CitationWSEAS INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING AND DATA BASES, 9, Cambridge, UK , 2010 – “Recent Advances in Artificial Intelligence, Knowledge Engineering and Database : proceedings of the 9th WSEAS International Conference Artificial Intelligence, Knowledge Engineering and Database (AIKED’10).” [S.l.] : WSEAS, 2010. ISBN 978-960-474-154-0. p. 334-338.
Abstract(s)The process of knowledge discovery in databases aims at the discovery of associations within data in a dataset. Data Mining is a central step of this process corresponding to the application of algorithms for identifying patterns in data. This paper presents the particular case of analysis of a dataset containing data associated with 227 patients submitted to an endoscopic thoracic sympathectomy, a treatment for primary palmar hyperhidrosis. Primary hyperhidrosis is characterized by an excessive sweating that appears as a consequence of a disorder of the sympathetic autonomous nervous system. The results achieved show an overall improvement of the patients’ quality of life, mainly associated with their emotional state.
TypeConference paper
URIhttp://hdl.handle.net/1822/11367
ISBN978-960-474-154-0
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:DSI - Engenharia da Programação e dos Sistemas Informáticos

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